Evolutionary Algorithms for Robust Density-Based Data Clustering
TL;DR: Two approaches to robust density-based clustering for relational data using evolutionary computation are investigated and found to be robust against outliers in data.
read more
Abstract: Density-based clustering methods are known to be robust against outliers in data; however, they are sensitive to user-specified parameters, the selection of which is not trivial. Moreover, relational data clustering is an area that has received considerably less attention than object data clustering. In this paper, two approaches to robust density-based clustering for relational data using evolutionary computation are investigated.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Where will the next emergency event occur? Predicting ambulance demand in emergency medical services using artificial intelligence
George Grekousis,Ye Liu +1 more
TL;DR: Assessing expected emergency events through the proposed method, would allow medical services to optimally locate ambulances in advance, reducing response time and thus increasing survival rates and public safety.
50
Evolutionary clustering algorithms for relational data
Amit Banerjee,Issam Abu-Mahfouz +1 more
TL;DR: Five evolutionary techniques are presented in this paper – three algorithms based on particle swarm optimization, the firefly algorithm and the composite differential evolution technique, which are tested on benchmark datasets from the UCI machine learning database.
5
References
•Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu +3 more
- 02 Aug 1996
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
20.3K
•Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Xiaowei Xu +3 more
- 01 Jan 1996
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
A Cluster Separation Measure
TL;DR: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster which can be used to infer the appropriateness of data partitions.
8.4K
OPTICS: ordering points to identify the clustering structure
Mihael Ankerst,Markus M. Breunig,Hans-Peter Kriegel,Jörg Sander +3 more
- 01 Jun 1999
TL;DR: A new algorithm is introduced for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure.
4.6K